1. Field
An embodiment of the invention relates to the field of supply chain management. In particular, the embodiment relates to the field of managing an inventory in a discrete component manufacturing supply chain based on model predictive control.
2. Background Information
Supply chains are found in many manufacturing and service industries. A manufacturing supply chain, also known as a demand network or value web, generally represents a network of interconnected manufacturing and distribution facilities that procure materials, transform the materials into intermediate and finished products, and distribute the finished products to customers. The structure of the supply chain may be organized and managed with a goal of maintaining a high level of customer service, minimizing costs, and maximizing profits. The supply chain that flourishes, and gains market share, generally favors customer service, for example by providing its customers the right product, in the right amount, at the right time, for the right price, and at the right place, while suppressing major costs, such as materials, production, storage, and transport.
Large cost reductions and increased profitability may generally be achieved through improved management of supply chains. Taking the semiconductor supply chain as an example, some experts predict that billions of dollars in cost reductions may be achieved through improved management of semiconductor supply chains. Some experts hold the belief that individual companies no longer compete against other individual companies, but rather supply chains compete against other supply chains. Accordingly, the quality management of a supply chain may represent an important and valuable factor in determining the success of a manufacturing enterprise.
Traditionally, supply chains and inventories thereof have often been managed through cost-optimal stochastic programming solutions from the field of operations research. These approaches are generally time consuming, and often involve evaluating and examining numerous “what-if” scenarios by highly skilled professionals. Additionally, these approaches are generally poorly suited for handling dynamically changing variables, such as consumer demand and materials supply dynamics. In supply chains, variables such as consumer demand for product may continually and dynamically change. Such changes in demand may outdate cost-optimal stochastic programming solutions and merit their re-evaluation, which may be time consuming and cost prohibitive.